A global record of annual urban dynamics (1992–2013) from nighttime lightsZhouYauthorLiXauthorAsrarGRauthorSmithSJauthorImhoffMauthor2018The nighttime light (NTL) observations from Defense Meteorological Satellite Program/Operational Linescane System (DMSP/OLS) offer great potentials to study urban dynamics from regional to global scales, for more than two decades. In this paper, we presented a new approach to develop spatially and temporally consistent global urban maps from 1992 to 2013, using the DMSP/OLS NTL observations. First, potential urban clusters were delineated using the NTL data and a segmentation method. Then, a quantile-based approach was used to remove rural and suburban areas sequentially in the potential urban clusters. Finally, the derived series of urban extents in the entire study period (1992–2013) were improved for temporal consistency. We found the percentage of global urban areas relative to the world's land surface area increased from 0.23% in 1992 to 0.53% in 2013. Asia is the continent with the most significant urban growth, worldwide. The time series of global urban maps were evaluated for the spatial agreement and temporal consistency using a variety of widely used independent land-cover products. This evaluation indicates that the proposed approach is robust and performs well in deriving global urban dynamics across different spatial scales, i.e., cluster, province (or state), country, and region. Moreover, this quantile-based approach is advantageous, compared with other methods used in previous studies, because it does not require additional data for enhancement or calibration. The new time series of urban maps from this study offer a new dataset for studying global urbanization during the past decades and unique information to explore potential future trajectories of urban development, which appears to be distinct for different nations/regions, globally. Such information is pre-requisite for achieving the sustainable development goals, and associated targets, during ensuing decades.Remote Sensingexported from refbase (http://alandb.darksky.org/show.php?record=2048), last updated on Mon, 29 Oct 2018 03:08:20 -0700texthttps://linkinghub.elsevier.com/retrieve/pii/S0034425718304632https://linkinghub.elsevier.com/retrieve/pii/S003442571830463210.1016/j.rse.2018.10.015Zhou_etal2018Remote Sensing of EnvironmentRemote Sensing of Environment2018continuingperiodicalacademic journal2192062200034-4257